best auction software

[Big Tymer]

So I’ll defer to you and maybe I’m too generous with my assumptions but you would think these would all modeled off PT translated to ABs or IP and then translated to all the categorical stats based on H%, BB%, K% type statistics (oversimplified here since I don’t feel like typing a ton ?)

[The Feral Slasher]

So I’ll defer to you and maybe I’m too generous with my assumptions but you would think these would all modeled off PT translated to ABs or IP and then translated to all the categorical stats based on H%, BB%, K% type statistics (oversimplified here since I don’t feel like typing a ton ?)

This is a really fascinating topic. I wonder how the projections from various websites differ in terms of accuracy from the fantasy baseball industry vs each other and also compared to what the MLB teams use. And how can you measure the accuracy ? Playing time and injuries are difficult to project sometimes...so are projections best done on plate appearance basis ? From a fantasy baseball perspective, does using one websites projections offer any advantage ? Is it better to average projections from various sites ? Or is that in the noise considering all the other variables ? If a tree falls in the forest and no one is there did it make a sound ? Inquiring minds want to know.

[Big Tymer]

It would indeed be an awesome study. I can hypothesize several possible approaches but the first that comes to mind would control for a population where expected playing time closely matched actual playing time (to control for player specific variance caused by injury). If say you isolated a subpopulation of just the players who hit their expected playing time projections (give or take) then you have somewhat a clean sample to then use for comparison purposes against all the main projections available. At that point, you could model for accuracy across sites probably as an index.

Not perfect but you would need to do something to just isolate a rep sample I think since there is no way to project outliers since by definition that outlier outcome would be outside the range of expected possibilities. I’d personally be more curious to see who is most accurate projecting when “noise” is eliminated basically.

[The Feral Slasher]

It would indeed be an awesome study. I can hypothesize several possible approaches but the first that comes to mind would control for a population where expected playing time closely matched actual playing time (to control for player specific variance caused by injury). If say you isolated a subpopulation of just the players who hit their expected playing time projections (give or take) then you have somewhat a clean sample to then use for comparison purposes against all the main projections available. At that point, you could model for accuracy across sites probably as an index.
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Not perfect but you would need to do something to just isolate a rep sample I think since there is no way to project outliers since by definition that outlier outcome would be outside the range of expected possibilities. I’d personally be more curious to see who is most accurate projecting when “noise” is eliminated basically.

Evaluating projections for a smaller population of guys that basically play a whole season would be interesting. Someone has probably already done it, I'm always amazed at the amount of analysis and info that is now available compared to the past.